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Advances in knowledge discovery and data mining = 25th Pacific-Asia Conference, PAKDD 2021, virtual event, May 11-14, 2021 : proceedings.. Part III /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in knowledge discovery and data mining/ edited by Kamal Karlapalem ... [et al.].
其他題名:	25th Pacific-Asia Conference, PAKDD 2021, virtual event, May 11-14, 2021 : proceedings.
其他題名:	PAKDD 2021
其他作者:	Karlapalem, Kamal.
團體作者:	PAKDD (Conference)
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xxiii, 434 p. :ill. (some col.), digital ;24 cm.
內容註:	Representation Learning and Embedding -- Episode Adaptive Embedding Networks for Few-shot Learning -- Universal Representation for Code -- Self-supervised Adaptive Aggregator Learning on Graph -- A Fast Algorithm for Simultaneous Sparse Approximation -- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning -- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification -- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models -- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network -- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning -- Self-supervised Graph Representation Learning with Variational Inference -- Manifold Approximation and Projection by Maximizing Graph Information -- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping -- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction -- Human-Understandable Decision Making for Visual Recognition -- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding -- Transferring Domain Knowledge with an Adviser in Continuous Tasks -- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach -- Quality Control for Hierarchical Classification with Incomplete Annotations -- Learning from Data -- Learning Discriminative Features using Multi-label Dual Space -- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering -- BanditRank: Learning to Rank Using Contextual Bandits -- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach -- Meta-Context Transformers for Domain-Specific Response Generation -- A Multi-task Kernel Learning Algorithm for Survival Analysis -- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection -- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction -- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning -- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition -- Reinforced Natural Language Inference for Distantly Supervised Relation Classification -- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction -- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function -- Incorporating Relational Knowledge in Explainable Fake News Detection -- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction.
Contained By:	Springer Nature eBook
標題:	Data mining - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-75768-7
ISBN:	9783030757687

Advances in knowledge discovery and data mining = 25th Pacific-Asia Conference, PAKDD 2021, virtual event, May 11-14, 2021 : proceedings.. Part III /
Advances in knowledge discovery and data mining25th Pacific-Asia Conference, PAKDD 2021, virtual event, May 11-14, 2021 : proceedings.Part III /[electronic resource] :PAKDD 2021edited by Kamal Karlapalem ... [et al.]. - Cham :Springer International Publishing :2021. - xxiii, 434 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,127140302-9743 ;. - Lecture notes in computer science ;12714..

Representation Learning and Embedding -- Episode Adaptive Embedding Networks for Few-shot Learning -- Universal Representation for Code -- Self-supervised Adaptive Aggregator Learning on Graph -- A Fast Algorithm for Simultaneous Sparse Approximation -- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning -- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification -- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models -- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network -- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning -- Self-supervised Graph Representation Learning with Variational Inference -- Manifold Approximation and Projection by Maximizing Graph Information -- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping -- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction -- Human-Understandable Decision Making for Visual Recognition -- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding -- Transferring Domain Knowledge with an Adviser in Continuous Tasks -- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach -- Quality Control for Hierarchical Classification with Incomplete Annotations -- Learning from Data -- Learning Discriminative Features using Multi-label Dual Space -- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering -- BanditRank: Learning to Rank Using Contextual Bandits -- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach -- Meta-Context Transformers for Domain-Specific Response Generation -- A Multi-task Kernel Learning Algorithm for Survival Analysis -- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection -- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction -- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning -- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition -- Reinforced Natural Language Inference for Distantly Supervised Relation Classification -- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction -- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function -- Incorporating Relational Knowledge in Explainable Fake News Detection -- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction.

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

ISBN: 9783030757687

Standard No.: 10.1007/978-3-030-75768-7doiSubjects--Topical Terms:

551626
Data mining
--Congresses.

LC Class. No.: QA76.9.D343 / P35 2021

Dewey Class. No.: 006.3

Advances in knowledge discovery and data mining = 25th Pacific-Asia Conference, PAKDD 2021, virtual event, May 11-14, 2021 : proceedings.. Part III /
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